WO2023128512A1 - Dispositif et procédé de guidage d'exercice par ia - Google Patents

Dispositif et procédé de guidage d'exercice par ia Download PDF

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Publication number
WO2023128512A1
WO2023128512A1 PCT/KR2022/021299 KR2022021299W WO2023128512A1 WO 2023128512 A1 WO2023128512 A1 WO 2023128512A1 KR 2022021299 W KR2022021299 W KR 2022021299W WO 2023128512 A1 WO2023128512 A1 WO 2023128512A1
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Prior art keywords
exercise
guide
motion trajectory
muscle
motion
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PCT/KR2022/021299
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English (en)
Korean (ko)
Inventor
유선경
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주식회사 디랙스
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Priority claimed from KR1020220183460A external-priority patent/KR20230100661A/ko
Application filed by 주식회사 디랙스 filed Critical 주식회사 디랙스
Publication of WO2023128512A1 publication Critical patent/WO2023128512A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/22—Ergometry; Measuring muscular strength or the force of a muscular blow
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
    • A63B71/06—Indicating or scoring devices for games or players, or for other sports activities

Definitions

  • the present invention is intended to provide the user with information about contraction and relaxation of muscles when the user exercises.
  • Weight exercise equipment is provided in various forms according to body parts for increasing muscle strength or purpose of use, and is mainly made to train the upper and lower bodies using hands or feet. The user may exercise by moving the selected weight through the exercise structure of the exercise device.
  • an exercise guide including information on muscle contraction and relaxation is provided in real time to improve exercise effects.
  • an exercise guide including breathing information is provided in real time to improve exercise effects.
  • the AI exercise guide device is at least one muscle that is activated when using the exercise machine, the contribution of each of the at least one muscle, and the motion trajectory generated when the exercise machine is properly used according to the criteria for use.
  • a storage unit for storing a guide; a sensing unit for detecting a motion trajectory corresponding to a user's movement of the exercise machine; an AI processing unit for determining whether the motion trajectory detected by the sensing unit and the motion trajectory guide coincide with each other when the exercise device is moved in at least one direction; and a motion guide unit for guiding the motion load or motion posture of the exercise device to be adjusted when the deviation between the detected motion trajectory and the motion trajectory guide exceeds a preset value.
  • the AI exercise guide method can improve the exercise effect by providing a exercise guide containing information on contraction and relaxation of muscles in real time when a user exercises using an exercise machine.
  • an exercise guide including breathing information may be provided in real time to improve exercise effects.
  • the AI exercise guide method is based on the motion trajectory information obtained when the user uses the exercise equipment when the user's exercise posture is wrong or the weight of the exercise machine used by the user is unsuitable for the user.
  • the exercise effect can be improved by providing a guide.
  • FIG. 1 shows a smart gym system in which an AI exercise guide device is used as a preferred embodiment of the present invention.
  • FIG. 2 shows an example of an exercise device in which an AI exercise guide method provided in a smart gym is implemented as a preferred embodiment of the present invention.
  • Figure 3 shows an internal configuration diagram of a smart gym system as a preferred embodiment of the present invention.
  • FIG. 4 shows an example of classifying motion trajectory big data as a preferred embodiment of the present invention.
  • Figure 5 shows an example of displaying at least one muscle and a breathing guide that are activated when performing an exercise on a display unit as a preferred embodiment of the present invention.
  • FIG. 6 shows an example of a motion trajectory detected in an exercise machine as a preferred embodiment of the present invention.
  • FIG. 7 shows an example in which at least one muscle activated when performing an exercise and an accumulated exercise amount are displayed in real time as another preferred embodiment of the present invention.
  • the AI exercise guide device is at least one muscle that is activated when using the exercise machine, the contribution of each of the at least one muscle, and the motion trajectory generated when the exercise machine is properly used according to the criteria for use.
  • a storage unit for storing a guide; a sensing unit for detecting a motion trajectory corresponding to a user's movement of the exercise machine; an AI processing unit for determining whether the motion trajectory detected by the sensing unit and the motion trajectory guide coincide with each other when the exercise device is moved in at least one direction; and a motion guide unit for guiding the motion load or motion posture of the exercise device to be adjusted when the deviation between the detected motion trajectory and the motion trajectory guide exceeds a preset value.
  • the AI exercise guide device is characterized in that it further comprises a display displaying the load added to at least one muscle that is activated when the user uses the exercise equipment based on the contribution.
  • the exercise guide unit displays information on the contraction or relaxation state of at least one muscle that is activated when using the exercise equipment in real time; characterized in that it further comprises.
  • a contraction guide or a relaxation guide is displayed in real time for the main muscle activated when the user uses the exercise device characterized by
  • the exercise guide unit displays an indicator for exhalation in the direction in which the main muscle is contracted and inhalation in the direction in which the main muscle is relaxed among at least one muscle activated when using the exercise machine as an indicator; It is characterized in that it further comprises.
  • the sensing unit is characterized in that it detects the angle of the joint when the user moves the exercise device.
  • the motion guide unit includes a display that displays information on the contraction or relaxation state of at least one muscle activated when using an exercise device based on the angle of the joint extracted in real time.
  • the motion guide unit adjusts the motion posture when the shape of the motion trajectory corresponding to the angle of the joint in a certain section detected by the sensing unit is different from the shape of the motion trajectory of the previously stored motion trajectory guide. It is characterized by guiding to do it.
  • the AI exercise guide method includes the steps of detecting a motion trajectory corresponding to a motion of a user moving an exercise device in a sensing unit; Determining whether the motion trajectory detected by the sensing unit when the AI processing unit moves the exercise device in at least one direction matches the motion trajectory guide generated when the exercise device previously stored in the storage unit is properly used according to the usage standard ; and guiding the motion guide unit to adjust the motion load or motion posture of the exercise device when the deviation between the detected motion trajectory and the motion trajectory guide exceeds a preset value.
  • the AI exercise guide method displays a load added to at least one muscle activated when the user uses the exercise machine based on the contribution on the display; characterized in that it comprises a .
  • the AI exercise guide method displays information on the contraction or relaxation state of at least one muscle that is activated when using the exercise equipment on the display in real time; It is characterized by including.
  • the AI exercise guide method uses an indicator for exhalation in the direction in which the main muscle is contracted and inhalation in the direction in which the main muscle is relaxed among at least one muscle activated when using exercise equipment on the display. It is characterized in that it includes; displaying step.
  • the sensing unit further detects the angle of the joint when the user moves the exercise device, and the display displays the number of at least one muscle that is activated when using the exercise device based on the detected angle of the joint. Characterized in that it comprises a; step of displaying information on the contraction or relaxation state in real time.
  • FIG. 1 shows a smart gym system in which an AI exercise guide device is used as a preferred embodiment of the present invention.
  • the smart gym system 100 includes a smart gym server 110, at least one exercise device 100a, 100b, 100c, ..., 100n, at least one user terminal 120 and a manager terminal 130. .
  • the smart gym server 110 can communicate with the first smart gym 112, the second smart gym 114, and the nth smart gym 116 in physically different locations, and the first smart gym At least one exercise device (100a, 100b) disposed in 112, at least one exercise device (100c) disposed in the second smart gym 114, and at least one disposed in the nth smart gym 116 Transmission and reception of data with the exercise equipment 100n is possible.
  • the smart gym provides the user's exercise record using exercise equipment to the smart gym server 110, and the smart gym server 110 learns and analyzes the user's exercise record to suit the user. It refers to a physical space that provides exercise prescriptions. Smart gyms can be implemented in fitness centers, gyms, and spaces equipped with exercise equipment.
  • Users who come to the smart gym to exercise can enter the smart gym after verifying their identity when entering or exiting the smart gym. For example, by tagging the user terminal 120 to an unmanned terminal such as a kiosk at the entrance of a smart gym by NFC (Near Field Communication) or RFID (Radio Frequency IDentification) method, the user can enter or exit after member verification, or unattended Access is possible after member confirmation is performed through biometric information confirmation such as face recognition in the terminal.
  • NFC Near Field Communication
  • RFID Radio Frequency IDentification
  • Information on the user whose membership has been verified may be transmitted from the smart gym server 200 to at least one of the exercise devices 100A, 100B, 100C, ..., 100N through the network.
  • the smart gym server 200 may transmit information about the user to the exercise equipment to which the terminal 120 is tagged by the user.
  • the information about the user is also used as the term user data and includes at least some or all of the user's gender, age, weight, height, BMI, and body fat percentage.
  • the smart gym server 110 provides a first user (USER A) and a second user (USER B) using each of at least one exercise device (100a, 100b) disposed in the smart gym 112, an exercise method suitable for each user. , exercise intensity, breathing method, exercise posture, etc. can be guided.
  • the smart gym server 110 may provide values such as a target weight and a recommended usage speed of each of the exercise equipments 100a and 100b.
  • the smart gym server 110 may receive exercise records of the first user (USER A) and the second user (USER B) using the exercise equipment (100a, 100b), respectively.
  • health information or log information such as heart rate, blood pressure, and pulse of the user may be further received from the user terminal 120 .
  • the smart gym server 110 may be implemented in the form of a cloud server.
  • the smart gym server 110 can integrate and manage information collected from each exercise equipment in the smart gym exercise center located in different locations. For example, the smart gym server 110 integrates and manages the details of the first user using the exercise equipment in the smart gym 112 in the first location and the details of using the exercise equipment in the smart gym 114 in the second location. can do.
  • At least one exercise device may be a stretching exercise machine, a weight exercise machine, or an aerobic exercise machine.
  • At least one exercise device (100a, 100b, 100c, ..., 100n) provides an exercise guide suitable for the user through a display attached to the exercise device or a display unit capable of communicating with the exercise device by wire or wireless.
  • a stretching device an exercise guide related to stretching to be used by a user is provided through a smart mirror capable of communicating with the stretching device by wire or wirelessly.
  • a motion guide may be provided using various output methods such as a speaker and vibration.
  • At least one exercise device may perform wired or wireless communication with the smart gym server 110, the user terminal 120, and the manager terminal 130.
  • the user terminal 120 may be implemented in the form of a smart phone, smart watch, handheld device, or wearable device.
  • the user terminal 120 may install an application for using the smart gym system.
  • the user terminal 120 may receive exercise sequence information and the like from the smart gym server 120 .
  • the exercise sequence refers to an exercise plan planned in consideration of the user's physical strength and exercise ability.
  • the exercise sequence includes information such as a list of exercise equipment to be used by the user, a target weight of each exercise machine, and the number of times of use.
  • the user uses at least one exercise device (100a, 100b, 100c, ..., 100n) in the smart gym system 100
  • the user performs communication through tagging such as NFC or RFID using the terminal 120, or Identity verification may be performed using the user's body characteristics.
  • the smart gym server 110 may transmit user data to the exercise equipment tagged by the user.
  • the smart gym system includes an AI exercise guide device 300 and a smart gym server 380.
  • the AI movement guide device 300 can communicate with the smart gym server 380, the user terminal 390, and an external server 388.
  • the AI exercise guide method may be implemented in the exercise equipment in the smart gym or the smart gym server 380.
  • the AI motion guide device 300 includes a processor 310, a sensing unit 320, a communication unit 340, a motion guide unit 360 and a display 370.
  • a storage unit (not shown), a camera unit 330 and an image processing unit 350 may be further included.
  • the processor 310 may further include an AI processing unit 312 as needed.
  • the AI processing unit 312 may detect a motion trajectory based on data sensed by the sensing unit 320 and analyze a difference between the detected motion trajectory and the motion trajectory guide.
  • the function of the AI processing unit 312 can also be implemented in the exercise guide processing unit 386 of the smart gym server 380.
  • the storage unit may receive and store necessary data through communication with the smart gym server 380, the user terminal 390 or an external server 388.
  • the AI exercise guide device 300 stores at least one muscle activated when using the exercise equipment, the contribution of each of the at least one muscle, and a motion trajectory guide generated when the exercise machine is accurately used in the correct posture in the storage unit. Or, it can be received through communication with the smart gym server (380).
  • FIG. 2 shows a shoulder press 200, which is an example of an exercise device in which an AI exercise guide device is installed.
  • the sensing unit 220 may be installed in the frame structure 213 of the exercise body 210 .
  • the frame structure 213 includes a base frame 231a, guide rails 231b, and connection lines 231c.
  • the sensing unit 220 irradiates a laser beam toward the fin structure 215, receives the reflected laser beam, and measures the distance D (S220) from the sensing unit 220 to the fin structure 215 in real time or It is measured in units of preset t time. Through this, the sensing unit 220 may detect at least one of the position, moving speed, and moving direction of the weight member 211 selected by the pin structure 215 in real time.
  • the sensing unit 220 measures the distance (D) (S220) to the pin structure 215 inserted in the weight plate, Based on this, the motion trajectory can be detected.
  • the sensing unit 220 may also be attached to an exercise device to detect an angle of a joint used when a user uses the exercise device. In this case, a mark indicating a reference point or the like may be attached to the user's joint. The sensing unit 220 may detect the angle of the joint in real time by sensing the mark attached to the joint.
  • this is only an example and various modifications are possible.
  • the display 230 may display a state of contraction or relaxation of at least one muscle activated when the user uses the shoulder press 200, and may also display a load applied to the at least one muscle.
  • the display 230 may also display in real time a contraction guide or a relaxation guide for the main muscles that are activated when the user uses the shoulder press 200 .
  • a breathing guide may be further displayed.
  • the contraction or relaxation state of at least one muscle activated when the user uses the shoulder press 200 refers to the contraction or relaxation state of the user's muscles detected by the sensing unit 220 .
  • the contraction guide or relaxation guide is a contraction-occurring muscle and contraction time, and a relaxation-occurring muscle and relaxation when the user uses the shoulder press 200 in a correct posture and meets the criteria refers to time
  • the AI processing unit 312 determines whether the motion trajectory detected by the sensing unit 320 and the motion trajectory guide received from the smart gym server match. In addition, the AI processing unit 312 determines whether the motion trajectory corresponding to the angle of the joint and the motion trajectory guide match. For the motion trajectory guide, refer to the description of FIG. 4 .
  • the motion trajectory of the 0-4° range of the knee joint coincides with the motion trajectory guide.
  • the section where the knee joint is 0-4° is the section where the gastrocnemius muscle is activated.
  • the motion trajectory of the range of 5-90° of the knee joint matches the motion trajectory guide.
  • the interval in which the knee joint is 5-90° is the interval in which the biceps femoris, semitendinosus, and hamstrings are activated.
  • the AI processing unit 312 determines that it is necessary to adjust the exercise posture when the shape of the motion trajectory corresponding to the angle of the joint in a certain section detected by the sensing unit and the shape of the motion trajectory guide are different. In this case, the AI processing unit 312 may further refer to the user's exercise posture detected by the image processing unit 350.
  • the AI processing unit 312 determines that there is a problem with the user's gastrocnemius muscle or an exercise posture related to the gastrocnemius muscle when a discrepancy occurs in the range of 0-4 ° for the knee joint.
  • the AI processing unit 312 determines that there is a problem with the biceps femoris, semitendinosus muscle, and hamstring muscle, or that there is a problem with the exercise posture related to the biceps femoris muscle, semitendinosus muscle, and hamstring muscle when discrepancies occur in the range of 5-90 ° of the knee joint. do.
  • the AI processing unit 312 also sets a preset value between the movement displacement of the motion trajectory corresponding to the angle of the joint in a certain section detected by the sensing unit and the movement displacement of the height or width of the motion trajectory guide received from the smart gym server. If it exceeds, it is determined that the size of the exercise load needs to be adjusted. For example, referring to FIG. 6, when the height H 612 of the user's motion trajectory detected by the sensing unit 320 is lower than the height h of the pre-stored motion trajectory guide by a predetermined ratio or more, lowering the size of the motion load decide what is necessary
  • the communication unit 340 may receive user input through the display 230 or transmit and receive user data from the user DB 382 of the smart gym server 380 .
  • the communication unit 340 can also communicate with an external server 388 .
  • the exercise guide unit 360 includes the user data received from the smart gym server 380, the target weight of the exercise machine, the exercise trajectory guide, the moving speed guide of the exercise machine, the breathing guide, and the main muscles activated when using the exercise machine. It is possible to provide information such as a contraction guide or a relaxation guide for the user.
  • the motion guide unit 360 may provide a motion guide based on the determination result of the AI processing unit 312 .
  • the motion guide unit 360 may guide the AI processing unit 312 to lower or increase the exercise load of the exercise device when the deviation between the motion trajectory detected by the sensing unit 320 and the motion trajectory guide exceeds a preset value.
  • the motion guide unit 360 may guide that a change in exercise posture is required.
  • the smart gym server 380 includes a user DB 382, a machine learning processing unit 384, and an exercise guide processing unit 386.
  • the user DB 382 stores and manages user data.
  • User data includes the user's gender, age, weight, height, BMI, and body fat percentage.
  • the machine learning processing unit 384 analyzes, accumulates, learns, and processes big data including user data obtained from the user DB and exercise records of exercise equipment used by users using the smart gym.
  • the smart gym server 380 may provide an AI exercise guide method by receiving data obtained from a sensing unit installed in an exercise device.
  • the smart gym server 380 stores at least one muscle that is activated when the exercise equipment is used in the storage unit (not shown), the contribution of each of the at least one muscle, and the exercise trajectory generated when the exercise equipment is properly used according to the usage standard. save the guide Then, after determining whether the motion trajectory obtained from the sensing data received by the motion guide processing unit 386 matches the motion trajectory guide, it may be guided to adjust the exercise load or exercise posture of the exercise device used by the user.
  • the smart gym server 380 is a mapping table that stores PMW estimated percentile information generated based on PMW (Personal Maximum Weight) data obtained from the population for each exercise machine. holds Table 1 shows PMW estimated percentile information for leg extension exercise equipment.
  • PMW estimated percentile value Population PMW Estimation (kg) PMW90 103.3 ... ... PMW50 67.3 PMW40 66.5 ... ... PMW10 24.6
  • PMW Personal Maximum Weight
  • PMW estimation represents a value obtained by estimating the user's PMW based on the estimated muscle strength value calculated through Equation 1.
  • the smart gym server 380 may determine PMW estimation for each exercise equipment.
  • the smart gym server 380 determines the PMW estimation of the exercise equipment to be used by the user based on the estimated muscle strength value and the estimated PMW percentile information. And, based on the PMW estimation , the initial target weight of the exercise equipment to be used by the user is automatically set.
  • the smart gym server 380 pre-stores PMW estimation percentile information for each exercise equipment as shown in Table 1.
  • the motion guide processing unit 386 sets the initial target weight of each exercise device based on the PMW estimation .
  • the exercise guide processing unit 386 may set an initial target weight according to exercise intensity or exercise purpose.
  • the exercise guide processing unit 386 may set the weight to a% of the estimated PMW when the exercise intensity is low, b% of the estimated PMW when the exercise intensity is medium, and c% of the estimated PMW when the exercise is high intensity.
  • a can be set to 20 to 40, b to 40 to 60, and c to 60 to 80.
  • various modifications are possible if this is only an example.
  • the motion guide processing unit 386 may provide the initial target weight of the exercise device to be used by the user to the motion guide unit 360 of the exercise device 300, and the motion guide unit 360 is displayed on the display unit 370 to be used by the user.
  • the initial target weight of the exercise equipment can be displayed.
  • the display unit 370 includes a display.
  • the smart gym server 380 may predict the maximum muscle strength value PMW individual reflecting the objectification index for each individual user. Then, the smart gym server 380 may update the target weight of the exercise equipment based on the predicted PMW individual . More specifically, the smart gym server 380 sets the initial target weight of the exercise equipment based on the PMW estimation , and when the objectification index for each user is obtained for a predetermined period, the individual user's objectification index is reflected to predict the PMW individual . After that, the target weight of the exercise equipment is updated based on the PMW individual .
  • the exercise guide processing unit 386 may display an updated target weight based on the PMW individual on the exercise equipment to be used by the user.
  • An example of the objectification index is an exercise record
  • the exercise record is the weight of the exercise machine identified when using the exercise machine, the number of repetitions (Reps), the number of sets, the exercise trajectory, the moving speed, and the number of repetitions per set (Reps). regularity, etc.
  • the machine learning processing unit 384 learns and processes the objectification index including the exercise records of the exercise equipment used by the user in the smart gym.
  • the machine learning processing unit 384 may update the PMW estimate to the PMW individual based on the objectification index of the exercise equipment that the user has used in the smart gym for a certain period of time.
  • the machine learning processing unit 384 updates the PMW individual to a value larger than the PMW estimate when the objectification index is equal to or greater than the first reference value, and the PMW individual to a value smaller than the PMW estimate when the objectification index is equal to or less than the second reference value .
  • the machine learning processing unit 384 determines whether the first user and the second user have the same PMW estimation as the first user and the second user having the same user gender, age, weight, height, BMI, and body fat percentage. It is possible to learn and predict individual PMW suitable for the first user and the second user for each exercise equipment used by the first user and the second user by further reflecting the individual objectification index including the exercise record of the user.
  • the machine learning processing unit 384 learns and processes the motion trajectory detected from the exercise machine. This will be described with reference to FIG. 6 .
  • the machine learning processing unit 384 determines the rising start point 611, the falling start point 613, the rising section speed V1 (S610), and the falling section speed V2 (S620) identified from the motion trajectory detected while the user is using the exercise equipment.
  • the average speed of the ascending section, the average speed of the descending section, and at least some of the height H 612 are used to determine the completeness of the motion trajectory, and the degree of completeness of the motion trajectory can be converted into a numerical value and converted into an objectification index.
  • the machine learning processing unit 384 may generate an objectification index from the user's exercise record.
  • the machine learning processing unit 384 uses the number of repetitions (Reps) as the objectification index
  • the regularity between the total number of repetitions constituting one set and the movement trajectories of each constitutes the one set
  • the completeness of the number of repetitions (Reps) is determined based on the execution time for performing all the number of iterations, and the degree of completeness of the number of repetitions (Reps) can be converted into a numerical value and used as an objectification index.
  • the machine learning processing unit 384 can provide a motion trajectory guide optimized for each user by classifying the motion trajectory big data. This will be described with reference to FIG. 4 .
  • the machine learning processing unit 384 classifies the exercise trajectory for a specific exercise machine obtained from the population into 7 patterns (410 to 470).
  • the x-axis represents time, and the y-axis represents standardized movement displacement.
  • the population initially targets n preset persons, but may continuously accumulate and use data of users using the smart gym.
  • the machine learning processing unit 384 can classify the motion trajectory patterns of the population and determine the proportion of the population belonging to each pattern.
  • the machine learning processing unit 384 selects a motion trajectory guide from among the analyzed 7 types of motion trajectory patterns 410 to 470. In this case, the machine learning processing unit selects the second motion trajectory pattern 420 and the sixth motion trajectory pattern 460 having the smallest deviation from the preset reference value trajectory 412 having the highest motion effect as a motion trajectory guide.
  • the machine learning processing unit 384 may select at least one or more motion trajectory guides 420 and 460 in consideration of a difference in motion trajectories generated by differences in height and physical condition for each user.
  • the second motion trace pattern 420 corresponds to 21.7% of the population
  • the sixth motion trace pattern 460 corresponds to 23.1% of the population.
  • the motion guide processing unit 386 selects a motion trajectory guide belonging to a pattern closest to the detected user's motion trajectory among at least one motion trajectory guides 420 and 460 obtained from the machine learning processing unit 384 . For example, when the movement displacement of the user's motion trajectory is smaller than 0.5, the second motion trajectory pattern 420 is selected and provided to the user as a motion trajectory guide. Alternatively, when the movement displacement of the user's motion trajectory is about 0.6, the sixth motion trajectory pattern 460 is selected and provided to the user as a motion trajectory guide.
  • the exercise guide processing unit 386 may also determine the change in the user's motion trajectory according to the exercise load, the number of times, and time. For example, the motion trajectory when the first user uses the chest press at 60 kg belongs to the second motion trajectory pattern 420, but when the first user uses the chest press at 70 kg, the motion trajectory is the first motion trajectory pattern ( 410), the motion guide processing unit 386 may determine that the load suitable for the first user is 60 kg. In this case, the motion guide processing unit 386 may guide the motion guide unit 360 to adjust the size of the exercise load.
  • the configuration of the movement guide processing unit 386 can also be implemented in the AI processing unit 312.
  • the smart gym server 380 stores information on exercise equipment corresponding to each body part, joints and muscles used for each exercise equipment in a storage unit, as shown in the example of Table 2. there is.
  • Table 2 shows an example of joint and muscle data for each exercise device corresponding to the body part 'lower body'.
  • the smart gym server 380 also stores information about at least one muscle that is activated when using the exercise equipment and the contribution of each of the at least one muscle, as shown in Tables 3 to 4.
  • the smart gym server 380 also stores information on at least one muscle used for each movement range of a joint for each exercise device.
  • the motion trajectory generated when the exercise equipment is properly used according to the usage standard is stored for each range of motion of the joint, and the motion trajectory stored for each range of motion can be provided to the user as a motion trajectory guide.
  • the motion trajectory guide refers to a motion trajectory generated when an exercise device is accurately used in a correct posture.
  • Figure 5 shows an example of displaying at least one muscle and a breathing guide that are activated when performing an exercise on a display unit as a preferred embodiment of the present invention.
  • the display unit displays information on the contraction 510a or relaxation 520a state of the biceps muscle 500 activated when the dumbbell is used based on the angle of the joint detected by the sensing unit in real time.
  • the display unit displays the contraction 510a of the biceps muscle 500 based on the angle of the joint, and displays the necessary breathing guide as an indicator 510b.
  • the display unit displays the relaxation 520a of the biceps brachii muscle 500 based on the angle of the joint, and the necessary breathing
  • the guide is indicated by an indicator 520b.
  • the breathing guide may be provided in various ways according to the exercise device or exercise method. For example, exhalation 510b when the user's muscle load is loaded, and inhalation 520b when the muscle load is reduced may be provided as a breathing guide. Whether or not a load is placed on the user's muscles can be determined from the motion trajectory.
  • the AIAI processing unit determines that the user's elbow joint has an injury or problem.
  • the exercise guide unit may guide the user on the exercise method 530 of supporting the weight of the dumbbell without using the elbow joint.
  • FIG. 7 shows an example in which at least one muscle activated when performing an exercise and an accumulated exercise amount are displayed in real time as another preferred embodiment of the present invention.
  • the AI processing unit may calculate the amount of exercise in real time based on the contribution of each of the at least one muscle that is activated when the user performs an exercise.
  • the display may display an amount of exercise added to at least one muscle or an amount of cumulative exercise.
  • the 'rectus femoris muscle 730a' which is an activated muscle, is displayed in color or shade on the display unit 710 .
  • the display unit 710 may display activated muscles in 2D or 3D. The user can intuitively understand the currently activated muscles while looking at the display unit 710 .
  • the cumulative momentum of the vastus lateralis, vastus medial, and vastus intermediate is calculated as 20kg*2*80%.
  • the AI processing unit calculates a residual momentum indicating a difference between the target momentum of the target muscle and the cumulative momentum of the target muscle when the user has not performed all of the exercise sequences.
  • the display unit 710 may display an exercise progress status 740 .
  • the exercise progress status 740 includes at least one of a target exercise amount 740a, an accumulated exercise amount 740b, and a remaining exercise amount 740c.
  • the display unit 710 digitizes and displays at least one muscle 730a, 730b, 730c, 730d and the cumulative exercise quantity 740b activated during exercise in real time.
  • the display unit 720 may display the activated at least one muscle and the cumulative momentum of each of the at least one muscle on the character.
  • at least one muscle activated through exercise and the accumulated momentum of each of the at least one muscle may be digitized and displayed on the character.
  • Methods according to embodiments of the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer readable medium.
  • the computer readable medium may include program instructions, data files, data structures, etc. alone or in combination.
  • Program instructions recorded on the medium may be those specially designed and configured for the present invention or those known and usable to those skilled in computer software.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Physical Education & Sports Medicine (AREA)
  • Medical Informatics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Physics & Mathematics (AREA)
  • Veterinary Medicine (AREA)
  • Engineering & Computer Science (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Public Health (AREA)
  • Physiology (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Dentistry (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

Selon un mode de réalisation préféré, la présente invention concerne un dispositif de guidage d'exercice par IA comprenant : une unité de stockage permettant de stocker un ou plusieurs muscles activés lors de l'utilisation d'un équipement d'exercice, le degré de contribution de chaque muscle parmi le ou les muscles, et un guide de trajectoire d'exercice généré lorsque l'équipement d'exercice est correctement utilisé selon les critères d'utilisation; une unité de détection permettant de détecter la trajectoire d'exercice correspondant au mouvement d'un utilisateur déplaçant l'équipement d'exercice; une unité de traitement par IA permettant de déterminer si la trajectoire d'exercice détectée dans l'unité de détection est cohérente avec le guide de trajectoire d'exercice lorsque l'équipement d'exercice est déplacé dans une ou plusieurs directions; et une unité de guidage d'exercice permettant le guidage de sorte qu'une charge d'exercice ou une pose d'exercice de l'équipement d'exercice soit réglée si l'écart entre la trajectoire d'exercice détectée et le guide de trajectoire d'exercice dépasse une valeur prédéfinie.
PCT/KR2022/021299 2021-12-28 2022-12-26 Dispositif et procédé de guidage d'exercice par ia WO2023128512A1 (fr)

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KR20210190367 2021-12-28
KR10-2021-0190368 2021-12-28
KR20210190368 2021-12-28
KR10-2021-0190367 2021-12-28
KR10-2022-0183460 2022-12-23
KR1020220183460A KR20230100661A (ko) 2021-12-28 2022-12-23 Ai운동가이드장치 및 방법

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20120014471A (ko) * 2010-08-09 2012-02-17 엘지전자 주식회사 운동가이드장치 및 운동가이드방법
KR101571363B1 (ko) * 2015-03-31 2015-11-24 (주)개선스포츠 운동기구
KR20190099555A (ko) * 2018-02-19 2019-08-28 주식회사 인프라웨어테크놀러지 웨이트 운동기구 및 운동 피드백 서비스를 제공하는 방법
KR20200022776A (ko) * 2018-08-23 2020-03-04 전자부품연구원 4d 아바타를 이용한 동작가이드장치 및 방법
KR20210147479A (ko) * 2020-05-29 2021-12-07 주식회사 피씨티 운동 가이드를 제공하는 근력 운동 머신 운동량 분석 및 자동측정 기기

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20120014471A (ko) * 2010-08-09 2012-02-17 엘지전자 주식회사 운동가이드장치 및 운동가이드방법
KR101571363B1 (ko) * 2015-03-31 2015-11-24 (주)개선스포츠 운동기구
KR20190099555A (ko) * 2018-02-19 2019-08-28 주식회사 인프라웨어테크놀러지 웨이트 운동기구 및 운동 피드백 서비스를 제공하는 방법
KR20200022776A (ko) * 2018-08-23 2020-03-04 전자부품연구원 4d 아바타를 이용한 동작가이드장치 및 방법
KR20210147479A (ko) * 2020-05-29 2021-12-07 주식회사 피씨티 운동 가이드를 제공하는 근력 운동 머신 운동량 분석 및 자동측정 기기

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